Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic...
详细信息
Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its design and the manner it is constructed, KFC holds great potential for implementation across a distributed system. The purpose of this thesis was to explain and extrapolate the afore mentioned IDS, highlight its effectiveness, and discuss the conceptual design of the distributed system for use in future research.
Intrusion detection systems (IDSs) are an integral component for the identification and mitigation of attacks on computing systems. Of these systems, vehicular ad hoc networks (VANETs) are particularly difficult to pr...
详细信息
Intrusion detection systems (IDSs) are an integral component for the identification and mitigation of attacks on computing systems. Of these systems, vehicular ad hoc networks (VANETs) are particularly difficult to protect due to the dynamic nature of their clients and the volume of information passed between them and their respective infrastructure. To meet these requirements, a spline-based intrusion detection system has been pioneered as a prospective solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows robust intrusion detection to occur.
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